IEEE Geoscience and Remote Sensing Letters最新文献_第4页

Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN 基于SCS-GAN的雷达PPI图像海杂波抑制

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3012523

Xiaoqian Mou, Xiaolong Chen, J. Guan, Yunlong Dong, Ningbo Liu

{"title":"Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN","authors":"Xiaoqian Mou, Xiaolong Chen, J. Guan, Yunlong Dong, Ningbo Liu","doi":"10.1109/lgrs.2020.3012523","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3012523","url":null,"abstract":"The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1886-1890"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3012523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46788870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Water Chlorophyll Estimation in an Urban Canal System With High-Resolution Remote Sensing Data 基于高分辨率遥感数据的城市渠系水体叶绿素估算

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3011074

Xiran Zhou, Jiawei Chen, Todd E. Rakstad, M. Ploughe, P. Tang

{"title":"Water Chlorophyll Estimation in an Urban Canal System With High-Resolution Remote Sensing Data","authors":"Xiran Zhou, Jiawei Chen, Todd E. Rakstad, M. Ploughe, P. Tang","doi":"10.1109/lgrs.2020.3011074","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3011074","url":null,"abstract":"Water quality, which is a key concern associated with large-scale canal operation and management, is vulnerable to the influences from short-term weather variations and artificial activities. Chlorophyll is one of the key indicators to measure the water quality and usability for drinking and irrigation in the canal system. However, previous research designed the state-of-the-art algorithms regarding water chlorophyll estimation using medium-resolution remote sensing data (e.g., Landsat), which has insufficient resolution to capture canals that are usually narrower than one pixel in such data. High-resolution imageries covering the whole canal network might include only either visible wavebands (i.e., red, green, blue bands) or cost thousands of dollars for an effective investigation on real-time water chlorophyll monitoring. Thus, the strategy designed for water chlorophyll analysis in a canal should consider an appropriate tradeoff among spatial resolution, the spectrum helpful for chlorophyll detection, and the financial burden. This letter presents our efforts on identifying and assessing the extent of the Planet data for measuring chlorophyll degree of canal waters. The experiments show that although Planet can represent the relative variation in water chlorophyll concentration, new algorithms are still necessary for accurate results regarding water chlorophyll variations in a canal system.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1876-1880"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43807209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Online Distributed Satellite Cooperative Observation Scheduling Algorithm Based on Multiagent Deep Reinforcement Learning 基于多智能体深度强化学习的在线分布式卫星协同观测调度算法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3009823

Li Dalin, W. Haijiao, Yang Zhen, Guan Yanfeng, Shen Shi

{"title":"An Online Distributed Satellite Cooperative Observation Scheduling Algorithm Based on Multiagent Deep Reinforcement Learning","authors":"Li Dalin, W. Haijiao, Yang Zhen, Guan Yanfeng, Shen Shi","doi":"10.1109/lgrs.2020.3009823","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3009823","url":null,"abstract":"The provision of real-time information services is one of the crucial functions of satellites. In comparison with the centralized scheduling, the distributed scheduling can provide better robustness and extendibility. However, the existing distributed satellite scheduling algorithms require a large amount of communication between satellites to coordinate tasks, which makes it difficult to support scheduling in real-time. This letter proposes a multiagent deep reinforcement learning (MADRL)-based method to solve the problem of scheduling real-time multisatellite cooperative observation. The method enables satellites to share their decision policy, but it is not necessary to share data on the decisions they make or data on their current internal state. The satellites can use the decision policy to infer the decisions of other satellites to decide whether to accept a task when they receive a new request for observations. In this way, our method can significantly reduce the communication overhead and improve the response time. The pillar of the architecture is a multiagent deep deterministic policy gradient network. Our simulation results show that the proposed method is stable and effective. In comparison with the Contract Net Protocol method, our algorithm can reduce the communication overhead and achieve better use of satellite resources.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1901-1905"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3009823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45056927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

3-D Marine CSEM Forward Modeling With General Anisotropy Using an Adaptive Finite-Element Method 基于一般各向异性的三维海洋电磁正演模拟

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3011743

Jiankai Li, Yuguo Li, Y. Liu, K. Spitzer, B. Han

引用次数: 2

SMAF-Net: Sharing Multiscale Adversarial Feature for High-Resolution Remote Sensing Imagery Semantic Segmentation 面向高分辨率遥感图像语义分割的多尺度对抗特征共享

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3011151

Jie Chen, Jingru Zhu, Geng Sun, Jianhui Li, M. Deng

{"title":"SMAF-Net: Sharing Multiscale Adversarial Feature for High-Resolution Remote Sensing Imagery Semantic Segmentation","authors":"Jie Chen, Jingru Zhu, Geng Sun, Jianhui Li, M. Deng","doi":"10.1109/lgrs.2020.3011151","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3011151","url":null,"abstract":"Semantic segmentation of high-resolution remote sensing imagery (HRSI) is a major task in remote sensing analysis. Although deep convolutional neural network (DCNN)-based semantic segmentation models have powerful capacity in pixel-wise classification, they still face challenge in obtaining intersemantic continuity and extraboundary accuracy because of the geo-object’s characteristic feature of diverse scales and various distributions in HRSI. Inspired by the transfer learning, in this study, we propose an efficient semantic segmentation framework named SMAF-Net, which shares multiscale adversarial features into a U-shaped semantic segmentation model. Specifically, it uses multiscale adversarial feature representation obtained from a well-trained generative adversarial network to grasp the pixel correlation and further improve the boundary accuracy of multiscale geo-objects. Comparison experiments on the Potsdam and Vaihingen data sets demonstrate that the proposed framework can achieve considerable improvement in the semantic segmentation of HRSI.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1921-1925"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42034558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

An Optimization Approach for Hourly Ozone Simulation: A Case Study in Chongqing, China 一种逐时臭氧模拟的优化方法——以重庆市为例

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3010416

Songyan Zhu, Qiaolin Zeng, Hao Zhu, Jian Xu, Jianbin Gu, Yongqian Wang, Liangfu Chen

{"title":"An Optimization Approach for Hourly Ozone Simulation: A Case Study in Chongqing, China","authors":"Songyan Zhu, Qiaolin Zeng, Hao Zhu, Jian Xu, Jianbin Gu, Yongqian Wang, Liangfu Chen","doi":"10.1109/lgrs.2020.3010416","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3010416","url":null,"abstract":"Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged $R^{2}$ is higher than 0.9 and the error is less than $5 ~mu text {g/m}^{3}$ .","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1871-1875"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43797425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Modeling of EM Scattering by Composite Surfaces Made of Wake Due to a Submerged Body and Wind-Driven Sea Waves 淹没体尾流和风驱动海浪复合表面的电磁散射建模

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3012164

Hai-Li Zhang, Xingyue Guo, Y. Sha, Xiao-Yang He, M. Xia

引用次数: 0

An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation 基于像素级和表示级域自适应的端到端遥感图像语义分割网络

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3010591

Lukui Shi, Ziyuan Wang, Bin Pan, Zhenwei Shi

{"title":"An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation","authors":"Lukui Shi, Ziyuan Wang, Bin Pan, Zhenwei Shi","doi":"10.1109/lgrs.2020.3010591","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3010591","url":null,"abstract":"It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1896-1900"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43030005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Adjustment of Measurements With Multiplicative Random Errors and Trends 具有乘法随机误差和趋势的测量平差

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3010827

Yun Shi, Peiliang Xu

引用次数: 8

Disk-Shaped Random Scatterers With Application to Model-Based PolSAR Decomposition 圆盘形随机散射体及其在基于模型的PolSAR分解中的应用

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI: 10.1109/lgrs.2020.3011917

Yanting Wang, T. Ainsworth, Jong-Sen Lee

引用次数: 6